Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

Amal Sasimohan; Mohamed Kutty Ibrahim; Sidharth C; Pranav P; Dr. S Gunasekaran; Ms. Anisree PG

doi:10.36948/ijfmr.2025.v07i06.63993

Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

Author(s)	Mr. Amal Sasimohan, Mr. Mohamed Kutty Ibrahim, Mr. Sidharth C, Mr. Pranav P, Dr. S Gunasekaran, Ms. Anisree PG
Country	India
Abstract	The identification of bird species is a crucial part of ecological research, the monitoring of biodiversity, and conservation efforts, however, manual methods cannot cope with the enormous amounts of images and sounds that are produced by modern recording technologies. The last few years of deep learning have brought about the automated bird species recognition to a great extent, especially when CNNs (Convolutional Neural Networks) and their variations like ResNet, DenseNet, MobileNet, and Xception are used. The review brings together the latest studies on the use of and comparative performance of these deep learning architectures in the classification of birds in image and sound. The works show that ensemble models, transfer learning, and data augmentation methods have a huge effect on the recognition performance, with the models like ResNet-50 and MobileNet reaching more than 94% accuracies in the case of large and imbalanced datasets. In the case of sound recognition, CNN models based on spectrograms like Xception make it possible to detect even the most subtle differences in the calls of hundreds of bird species very well, even when the surrounding sounds are difficult to manage. The major challenges include differences within the same species (intra-class variability), similarity between different species (inter-class similarity), environmental noise, and the lack of diverse and specific datasets. The review offers up ways to go on with the combination of outcomes from different sources of data, the use of transfer learning, and the creation of scalable systems as these will increase the accuracy and the usability of the automated bird species identification technology further.
Keywords	Bird species identification, Deep learning, Convolutional Neural Networks (CNN), Transfer learning, Ensemble learning, Audio classification, Image classification, ResNet-50, MobileNet, DenseNet, Xception, Biodiversity monitoring, Data augmentation, Fine-grained classification, Ecological informatics
Field	Biology > Zoology
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-20
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.63993

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Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images

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